Data processing: Depending on the type of the uploaded data, different data processing options are available (details).
This is followed by data normalization steps including normalization by constant sum, normalization by a reference sample/feature,
sample specific normalization, auto/Pareto/range scaling, etc.

Functional enrichment analysis: The service performs metatolite set enrichment analysis (MSEA) for human and mammalian species.
It can accept a list of compound names, a list of compound names with concentrations, or a concentration table. The analysis is based
on several libraries containing ~6300 groups of biologically meaningful metabolite sets collected primarily from human studies;

Time series and Two-factor data analysis: The service currently supports clustering and visualization (including
interactive 3D PCA visualization and two-way heatmaps with hierarchical clustering),
two-way ANOVA for univariate two-factor analysis, multivariate empirical Bayes time-series analysis (MEBA)
for detecting distinctive temporal profiles across different experimental conditions, and ANOVA-simultaneous component analysis (ASCA)
for identification of major patterns associated with each experimental factor (and their interactions);

Biomarker analysis: The service provides receiver operating characteristic (ROC) curve based
approach for evaluating the performance of potential biomarkers. It offers classical univariate ROC analysis
as well as more modern multivariate ROC curve analysis based on PLS-DA, SVM or Random Forests. In addition, users can
manually pick biomarkers or to set up hold-out samples for flexible evaluation and validation;

Sample size and power analysis: Users can upload a dataset either from a pilot study or from a similar study
to compute the minimum number of samples required to detect the effect within a certain degree of confidence,
as well as to estimate the power of the current study design.

Joint pathway analysis: The service allows users to simultaneously analyze genes and metabolites of interest
within the context of metabolic pathways. Only data from human, mouse and rat are supported currently.

MS peaks to pathway activities: Users can upload LC-MS peaks to perform metabolic pathway enrichment analysis and visual exploration based on
the well-established mummichog and GSEA algorithms. It currently supports 21 organisms including Human, Mouse, Zebrafish, C. elegans, and other species.

Network explorer: Users can upload one or two lists of metabolites, genes, or KEGG orthologs (i.e. generated from metagenomics),
and then visually explore these molecules of interest within the context of biological networks such as
KEGG global metabolic network, as well as several networks created based on known associations between genes, metabolites, and diseases.

Biomarker meta-analysis: Users can upload several metabolomics data sets obtained under comparable conditions to identify robust
biomarkers across multiple studies. It currently supports meta-analysis approaches based on p-value combination, vote counts
and direct merging. The results can be explored in interactive Venn diagram.

Image generation: Important images can be re-produced in high resolution in various format such as .png, .tiff, .ps, etc for
publication purposes

Report generation: Upon completion, a comprehensive PDF report will be generated documenting each step performed along with corresponding tabular and graphical
results. The processed data and images are also available for download.